Overview

Dataset statistics

Number of variables24
Number of observations3533892
Missing cells549641
Missing cells (%)0.6%
Duplicate rows424
Duplicate rows (%)< 0.1%
Total size in memory674.0 MiB
Average record size in memory200.0 B

Variable types

Categorical13
Numeric8
Text3

Alerts

activity_year has constant value ""Constant
Dataset has 424 (< 0.1%) duplicate rowsDuplicates
mortgage_term is highly imbalanced (70.9%)Imbalance
loan_outcome is highly imbalanced (56.3%)Imbalance
property_value_ratio has 155646 (4.4%) missing valuesMissing
combined_loan_to_value_ratio has 188622 (5.3%) missing valuesMissing
metro_name has 131511 (3.7%) missing valuesMissing
income is highly skewed (γ1 = 636.5541623)Skewed
loan_amount is highly skewed (γ1 = 1324.67877)Skewed
property_value_ratio is highly skewed (γ1 = 1604.288336)Skewed
combined_loan_to_value_ratio is highly skewed (γ1 = 1654.312096)Skewed

Reproduction

Analysis started2024-04-08 16:02:15.766564
Analysis finished2024-04-08 16:07:09.256306
Duration4 minutes and 53.49 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

race
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.9 MiB
White
2206416 
Latino
442455 
Race NA
379947 
Black
252901 
Asian
229237 
Other values (2)
 
22936

Length

Max length16
Median length5
Mean length5.4068254
Min length5

Characters and Unicode

Total characters19107137
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWhite
2nd rowWhite
3rd rowWhite
4th rowWhite
5th rowWhite

Common Values

ValueCountFrequency (%)
White 2206416
62.4%
Latino 442455
 
12.5%
Race NA 379947
 
10.8%
Black 252901
 
7.2%
Asian 229237
 
6.5%
Native American 16968
 
0.5%
Pacific Islander 5968
 
0.2%

Length

2024-04-08T11:07:09.314508image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-08T11:07:09.392028image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
white 2206416
56.0%
latino 442455
 
11.2%
race 379947
 
9.7%
na 379947
 
9.7%
black 252901
 
6.4%
asian 229237
 
5.8%
native 16968
 
0.4%
american 16968
 
0.4%
pacific 5968
 
0.2%
islander 5968
 
0.2%

Most occurring characters

ValueCountFrequency (%)
i 2923980
15.3%
t 2665839
14.0%
e 2626267
13.7%
W 2206416
11.5%
h 2206416
11.5%
a 1350412
7.1%
n 694628
 
3.6%
c 661752
 
3.5%
A 626152
 
3.3%
L 442455
 
2.3%
Other values (15) 2702820
14.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 14387532
75.3%
Uppercase Letter 4316722
 
22.6%
Space Separator 402883
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 2923980
20.3%
t 2665839
18.5%
e 2626267
18.3%
h 2206416
15.3%
a 1350412
9.4%
n 694628
 
4.8%
c 661752
 
4.6%
o 442455
 
3.1%
l 258869
 
1.8%
k 252901
 
1.8%
Other values (6) 304013
 
2.1%
Uppercase Letter
ValueCountFrequency (%)
W 2206416
51.1%
A 626152
 
14.5%
L 442455
 
10.2%
N 396915
 
9.2%
R 379947
 
8.8%
B 252901
 
5.9%
P 5968
 
0.1%
I 5968
 
0.1%
Space Separator
ValueCountFrequency (%)
402883
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18704254
97.9%
Common 402883
 
2.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 2923980
15.6%
t 2665839
14.3%
e 2626267
14.0%
W 2206416
11.8%
h 2206416
11.8%
a 1350412
7.2%
n 694628
 
3.7%
c 661752
 
3.5%
A 626152
 
3.3%
L 442455
 
2.4%
Other values (14) 2299937
12.3%
Common
ValueCountFrequency (%)
402883
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19107137
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 2923980
15.3%
t 2665839
14.0%
e 2626267
13.7%
W 2206416
11.5%
h 2206416
11.5%
a 1350412
7.1%
n 694628
 
3.6%
c 661752
 
3.5%
A 626152
 
3.3%
L 442455
 
2.3%
Other values (15) 2702820
14.1%

sex
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.9 MiB
Male
2123247 
Female
1202992 
NA
 
206101
Marked both
 
1552

Length

Max length11
Median length4
Mean length4.5672629
Min length2

Characters and Unicode

Total characters16140214
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowMale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male 2123247
60.1%
Female 1202992
34.0%
NA 206101
 
5.8%
Marked both 1552
 
< 0.1%

Length

2024-04-08T11:07:09.468390image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-08T11:07:09.531287image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
male 2123247
60.1%
female 1202992
34.0%
na 206101
 
5.8%
marked 1552
 
< 0.1%
both 1552
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 4530783
28.1%
a 3327791
20.6%
l 3326239
20.6%
M 2124799
13.2%
F 1202992
 
7.5%
m 1202992
 
7.5%
N 206101
 
1.3%
A 206101
 
1.3%
r 1552
 
< 0.1%
k 1552
 
< 0.1%
Other values (6) 9312
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12398669
76.8%
Uppercase Letter 3739993
 
23.2%
Space Separator 1552
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4530783
36.5%
a 3327791
26.8%
l 3326239
26.8%
m 1202992
 
9.7%
r 1552
 
< 0.1%
k 1552
 
< 0.1%
d 1552
 
< 0.1%
b 1552
 
< 0.1%
o 1552
 
< 0.1%
t 1552
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
M 2124799
56.8%
F 1202992
32.2%
N 206101
 
5.5%
A 206101
 
5.5%
Space Separator
ValueCountFrequency (%)
1552
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16138662
> 99.9%
Common 1552
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4530783
28.1%
a 3327791
20.6%
l 3326239
20.6%
M 2124799
13.2%
F 1202992
 
7.5%
m 1202992
 
7.5%
N 206101
 
1.3%
A 206101
 
1.3%
r 1552
 
< 0.1%
k 1552
 
< 0.1%
Other values (5) 7760
 
< 0.1%
Common
ValueCountFrequency (%)
1552
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16140214
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 4530783
28.1%
a 3327791
20.6%
l 3326239
20.6%
M 2124799
13.2%
F 1202992
 
7.5%
m 1202992
 
7.5%
N 206101
 
1.3%
A 206101
 
1.3%
r 1552
 
< 0.1%
k 1552
 
< 0.1%
Other values (6) 9312
 
0.1%

co_applicant
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.9 MiB
No co-applicant
1966616 
Co-applicant
1558662 
NA
 
8614

Length

Max length15
Median length15
Mean length13.645129
Min length2

Characters and Unicode

Total characters48220412
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo co-applicant
2nd rowNo co-applicant
3rd rowCo-applicant
4th rowNo co-applicant
5th rowNo co-applicant

Common Values

ValueCountFrequency (%)
No co-applicant 1966616
55.7%
Co-applicant 1558662
44.1%
NA 8614
 
0.2%

Length

2024-04-08T11:07:09.607105image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-08T11:07:09.672732image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
co-applicant 3525278
64.1%
no 1966616
35.8%
na 8614
 
0.2%

Most occurring characters

ValueCountFrequency (%)
a 7050556
14.6%
p 7050556
14.6%
o 5491894
11.4%
c 5491894
11.4%
- 3525278
7.3%
l 3525278
7.3%
i 3525278
7.3%
n 3525278
7.3%
t 3525278
7.3%
N 1975230
 
4.1%
Other values (3) 3533892
7.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 39186012
81.3%
Uppercase Letter 3542506
 
7.3%
Dash Punctuation 3525278
 
7.3%
Space Separator 1966616
 
4.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 7050556
18.0%
p 7050556
18.0%
o 5491894
14.0%
c 5491894
14.0%
l 3525278
9.0%
i 3525278
9.0%
n 3525278
9.0%
t 3525278
9.0%
Uppercase Letter
ValueCountFrequency (%)
N 1975230
55.8%
C 1558662
44.0%
A 8614
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
- 3525278
100.0%
Space Separator
ValueCountFrequency (%)
1966616
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 42728518
88.6%
Common 5491894
 
11.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 7050556
16.5%
p 7050556
16.5%
o 5491894
12.9%
c 5491894
12.9%
l 3525278
8.3%
i 3525278
8.3%
n 3525278
8.3%
t 3525278
8.3%
N 1975230
 
4.6%
C 1558662
 
3.6%
Common
ValueCountFrequency (%)
- 3525278
64.2%
1966616
35.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48220412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 7050556
14.6%
p 7050556
14.6%
o 5491894
11.4%
c 5491894
11.4%
- 3525278
7.3%
l 3525278
7.3%
i 3525278
7.3%
n 3525278
7.3%
t 3525278
7.3%
N 1975230
 
4.1%
Other values (3) 3533892
7.3%

age
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.9 MiB
25 through 34
1134547 
35 through 44
951797 
45 through 54
615512 
55 through 64
398048 
Less than 25
195357 
Other values (3)
238631 

Length

Max length15
Median length13
Mean length12.972681
Min length12

Characters and Unicode

Total characters45844053
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row25 through 34
2nd row25 through 34
3rd row25 through 34
4th rowLess than 25
5th row25 through 34

Common Values

ValueCountFrequency (%)
25 through 34 1134547
32.1%
35 through 44 951797
26.9%
45 through 54 615512
17.4%
55 through 64 398048
 
11.3%
Less than 25 195357
 
5.5%
65 through 74 188633
 
5.3%
Greater than 74 48816
 
1.4%
Not Applicable 1182
 
< 0.1%

Length

2024-04-08T11:07:09.742636image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-08T11:07:09.810300image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
through 3288537
31.0%
25 1329904
12.5%
34 1134547
 
10.7%
35 951797
 
9.0%
44 951797
 
9.0%
45 615512
 
5.8%
54 615512
 
5.8%
55 398048
 
3.8%
64 398048
 
3.8%
than 244173
 
2.3%
Other values (6) 672619
 
6.3%

Most occurring characters

ValueCountFrequency (%)
7066602
15.4%
h 6821247
14.9%
4 4904662
10.7%
5 4497454
9.8%
t 3582708
7.8%
r 3386169
7.4%
o 3289719
7.2%
u 3288537
7.2%
g 3288537
7.2%
3 2086344
 
4.6%
Other values (16) 3632074
7.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 24888420
54.3%
Decimal Number 13642494
29.8%
Space Separator 7066602
 
15.4%
Uppercase Letter 246537
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
h 6821247
27.4%
t 3582708
14.4%
r 3386169
13.6%
o 3289719
13.2%
u 3288537
13.2%
g 3288537
13.2%
s 390714
 
1.6%
a 294171
 
1.2%
e 294171
 
1.2%
n 244173
 
1.0%
Other values (5) 8274
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
4 4904662
36.0%
5 4497454
33.0%
3 2086344
15.3%
2 1329904
 
9.7%
6 586681
 
4.3%
7 237449
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
L 195357
79.2%
G 48816
 
19.8%
N 1182
 
0.5%
A 1182
 
0.5%
Space Separator
ValueCountFrequency (%)
7066602
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 25134957
54.8%
Common 20709096
45.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
h 6821247
27.1%
t 3582708
14.3%
r 3386169
13.5%
o 3289719
13.1%
u 3288537
13.1%
g 3288537
13.1%
s 390714
 
1.6%
a 294171
 
1.2%
e 294171
 
1.2%
n 244173
 
1.0%
Other values (9) 254811
 
1.0%
Common
ValueCountFrequency (%)
7066602
34.1%
4 4904662
23.7%
5 4497454
21.7%
3 2086344
 
10.1%
2 1329904
 
6.4%
6 586681
 
2.8%
7 237449
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45844053
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7066602
15.4%
h 6821247
14.9%
4 4904662
10.7%
5 4497454
9.8%
t 3582708
7.8%
r 3386169
7.4%
o 3289719
7.2%
u 3288537
7.2%
g 3288537
7.2%
3 2086344
 
4.6%
Other values (16) 3632074
7.9%

income
Real number (ℝ)

SKEWED 

Distinct3447
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean112.50465
Minimum1
Maximum365001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size53.9 MiB
2024-04-08T11:07:09.905041image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile32
Q155
median83
Q3128
95-th percentile270
Maximum365001
Range365000
Interquartile range (IQR)73

Descriptive statistics

Standard deviation298.97478
Coefficient of variation (CV)2.6574438
Kurtosis679755.11
Mean112.50465
Median Absolute Deviation (MAD)33
Skewness636.55416
Sum3.9757928 × 108
Variance89385.921
MonotonicityNot monotonic
2024-04-08T11:07:09.995553image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 44028
 
1.2%
50 41831
 
1.2%
52 39982
 
1.1%
65 38563
 
1.1%
55 38139
 
1.1%
62 37488
 
1.1%
70 36671
 
1.0%
75 36475
 
1.0%
48 36103
 
1.0%
42 35587
 
1.0%
Other values (3437) 3149025
89.1%
ValueCountFrequency (%)
1 510
< 0.1%
2 830
< 0.1%
3 1132
< 0.1%
4 1156
< 0.1%
5 1145
< 0.1%
6 1004
< 0.1%
7 896
< 0.1%
8 871
< 0.1%
9 880
< 0.1%
10 901
< 0.1%
ValueCountFrequency (%)
365001 1
< 0.1%
167923 1
< 0.1%
139000 1
< 0.1%
100000 1
< 0.1%
94000 1
< 0.1%
87360 1
< 0.1%
70844 1
< 0.1%
54000 1
< 0.1%
46560 1
< 0.1%
43316 1
< 0.1%

loan_amount
Real number (ℝ)

SKEWED 

Distinct668
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean288066.53
Minimum5000
Maximum1.106255 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size53.9 MiB
2024-04-08T11:07:10.091235image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum5000
5-th percentile85000
Q1155000
median235000
Q3345000
95-th percentile645000
Maximum1.106255 × 109
Range1.10625 × 109
Interquartile range (IQR)190000

Descriptive statistics

Standard deviation672267.5
Coefficient of variation (CV)2.3337231
Kurtosis2111981.5
Mean288066.53
Median Absolute Deviation (MAD)90000
Skewness1324.6788
Sum1.017996 × 1012
Variance4.5194359 × 1011
MonotonicityNot monotonic
2024-04-08T11:07:10.179591image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
205000 124758
 
3.5%
165000 121505
 
3.4%
155000 121333
 
3.4%
175000 118551
 
3.4%
185000 118524
 
3.4%
225000 117437
 
3.3%
195000 114156
 
3.2%
215000 112379
 
3.2%
145000 110573
 
3.1%
135000 104821
 
3.0%
Other values (658) 2369855
67.1%
ValueCountFrequency (%)
5000 1399
 
< 0.1%
15000 2342
 
0.1%
25000 5220
 
0.1%
35000 10049
 
0.3%
45000 17307
 
0.5%
55000 31686
0.9%
65000 40960
1.2%
75000 50689
1.4%
85000 59398
1.7%
95000 61616
1.7%
ValueCountFrequency (%)
1106255000 1
< 0.1%
410475000 1
< 0.1%
46025000 1
< 0.1%
29005000 1
< 0.1%
25005000 1
< 0.1%
24005000 1
< 0.1%
18005000 1
< 0.1%
17555000 1
< 0.1%
17005000 1
< 0.1%
16505000 1
< 0.1%

property_value_ratio
Real number (ℝ)

MISSING  SKEWED 

Distinct11686
Distinct (%)0.3%
Missing155646
Missing (%)4.4%
Infinite0
Infinite (%)0.0%
Mean1.397305
Minimum0.008
Maximum12967.896
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size53.9 MiB
2024-04-08T11:07:10.259991image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.008
5-th percentile0.557
Q10.886
median1.174
Q31.611
95-th percentile2.842
Maximum12967.896
Range12967.888
Interquartile range (IQR)0.725

Descriptive statistics

Standard deviation7.4203197
Coefficient of variation (CV)5.3104512
Kurtosis2769149.4
Mean1.397305
Median Absolute Deviation (MAD)0.339
Skewness1604.2883
Sum4720439.9
Variance55.061145
MonotonicityNot monotonic
2024-04-08T11:07:10.345762image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.134 6770
 
0.2%
0.942 6707
 
0.2%
1.057 6630
 
0.2%
0.903 5988
 
0.2%
1.018 5853
 
0.2%
0.98 5744
 
0.2%
0.994 5651
 
0.2%
1.229 5611
 
0.2%
1.318 5424
 
0.2%
1.02 5322
 
0.2%
Other values (11676) 3318546
93.9%
(Missing) 155646
 
4.4%
ValueCountFrequency (%)
0.008 1
 
< 0.1%
0.009 1
 
< 0.1%
0.01 1
 
< 0.1%
0.011 2
 
< 0.1%
0.013 3
 
< 0.1%
0.014 3
 
< 0.1%
0.016 2
 
< 0.1%
0.017 17
< 0.1%
0.018 1
 
< 0.1%
0.019 8
< 0.1%
ValueCountFrequency (%)
12967.896 1
< 0.1%
3010.999 1
< 0.1%
1832.974 1
< 0.1%
646.663 1
< 0.1%
486.491 1
< 0.1%
454.295 1
< 0.1%
418.116 1
< 0.1%
390.051 1
< 0.1%
336.373 1
< 0.1%
319.423 1
< 0.1%

mortgage_term
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.9 MiB
30 year mortgage
3190855 
Less than 30 years
 
208431
NA
 
107144
More than 30 years
 
27462

Length

Max length18
Median length16
Mean length15.709038
Min length2

Characters and Unicode

Total characters55514042
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNA
2nd rowNA
3rd rowNA
4th rowNA
5th rowNA

Common Values

ValueCountFrequency (%)
30 year mortgage 3190855
90.3%
Less than 30 years 208431
 
5.9%
NA 107144
 
3.0%
More than 30 years 27462
 
0.8%

Length

2024-04-08T11:07:10.427974image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-08T11:07:10.500672image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
30 3426748
32.3%
year 3190855
30.0%
mortgage 3190855
30.0%
than 235893
 
2.2%
years 235893
 
2.2%
less 208431
 
2.0%
na 107144
 
1.0%
more 27462
 
0.3%

Most occurring characters

ValueCountFrequency (%)
7089389
12.8%
e 6853496
12.3%
a 6853496
12.3%
r 6645065
12.0%
g 6381710
11.5%
3 3426748
6.2%
0 3426748
6.2%
t 3426748
6.2%
y 3426748
6.2%
o 3218317
5.8%
Other values (8) 4765577
8.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 41120976
74.1%
Space Separator 7089389
 
12.8%
Decimal Number 6853496
 
12.3%
Uppercase Letter 450181
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6853496
16.7%
a 6853496
16.7%
r 6645065
16.2%
g 6381710
15.5%
t 3426748
8.3%
y 3426748
8.3%
o 3218317
7.8%
m 3190855
7.8%
s 652755
 
1.6%
h 235893
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
L 208431
46.3%
N 107144
23.8%
A 107144
23.8%
M 27462
 
6.1%
Decimal Number
ValueCountFrequency (%)
3 3426748
50.0%
0 3426748
50.0%
Space Separator
ValueCountFrequency (%)
7089389
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 41571157
74.9%
Common 13942885
 
25.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6853496
16.5%
a 6853496
16.5%
r 6645065
16.0%
g 6381710
15.4%
t 3426748
8.2%
y 3426748
8.2%
o 3218317
7.7%
m 3190855
7.7%
s 652755
 
1.6%
h 235893
 
0.6%
Other values (5) 686074
 
1.7%
Common
ValueCountFrequency (%)
7089389
50.8%
3 3426748
24.6%
0 3426748
24.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 55514042
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7089389
12.8%
e 6853496
12.3%
a 6853496
12.3%
r 6645065
12.0%
g 6381710
11.5%
3 3426748
6.2%
0 3426748
6.2%
t 3426748
6.2%
y 3426748
6.2%
o 3218317
5.8%
Other values (8) 4765577
8.6%

credit_model
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.9 MiB
Equifax
1080038 
TransUnion
1023869 
Experian
861470 
NA
354371 
More than one
167871 
Other values (2)
 
46273

Length

Max length13
Median length10
Mean length7.8729882
Min length2

Characters and Unicode

Total characters27822290
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNA
2nd rowNA
3rd rowNA
4th rowNA
5th rowNA

Common Values

ValueCountFrequency (%)
Equifax 1080038
30.6%
TransUnion 1023869
29.0%
Experian 861470
24.4%
NA 354371
 
10.0%
More than one 167871
 
4.8%
Other 41701
 
1.2%
Vantage 4572
 
0.1%

Length

2024-04-08T11:07:10.570908image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-08T11:07:10.636402image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
equifax 1080038
27.9%
transunion 1023869
26.5%
experian 861470
22.3%
na 354371
 
9.2%
more 167871
 
4.3%
than 167871
 
4.3%
one 167871
 
4.3%
other 41701
 
1.1%
vantage 4572
 
0.1%

Most occurring characters

ValueCountFrequency (%)
n 4273391
15.4%
a 3142392
11.3%
i 2965377
10.7%
r 2094911
 
7.5%
E 1941508
 
7.0%
x 1941508
 
7.0%
o 1359611
 
4.9%
e 1243485
 
4.5%
u 1080038
 
3.9%
q 1080038
 
3.9%
Other values (14) 6700031
24.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22574416
81.1%
Uppercase Letter 4912132
 
17.7%
Space Separator 335742
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 4273391
18.9%
a 3142392
13.9%
i 2965377
13.1%
r 2094911
9.3%
x 1941508
8.6%
o 1359611
 
6.0%
e 1243485
 
5.5%
u 1080038
 
4.8%
q 1080038
 
4.8%
f 1080038
 
4.8%
Other values (5) 2313627
10.2%
Uppercase Letter
ValueCountFrequency (%)
E 1941508
39.5%
T 1023869
20.8%
U 1023869
20.8%
A 354371
 
7.2%
N 354371
 
7.2%
M 167871
 
3.4%
O 41701
 
0.8%
V 4572
 
0.1%
Space Separator
ValueCountFrequency (%)
335742
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 27486548
98.8%
Common 335742
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 4273391
15.5%
a 3142392
11.4%
i 2965377
10.8%
r 2094911
 
7.6%
E 1941508
 
7.1%
x 1941508
 
7.1%
o 1359611
 
4.9%
e 1243485
 
4.5%
u 1080038
 
3.9%
q 1080038
 
3.9%
Other values (13) 6364289
23.2%
Common
ValueCountFrequency (%)
335742
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27822290
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 4273391
15.4%
a 3142392
11.3%
i 2965377
10.7%
r 2094911
 
7.5%
E 1941508
 
7.0%
x 1941508
 
7.0%
o 1359611
 
4.9%
e 1243485
 
4.5%
u 1080038
 
3.9%
q 1080038
 
3.9%
Other values (14) 6700031
24.1%
Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.9 MiB
Healthy (<36%)
1359589 
Manageable (36-42%)
888042 
Unmanageable (43-49%)
822358 
Struggling (>50%)
326996 
Exempt
 
100070

Length

Max length21
Median length19
Mean length16.811376
Min length2

Characters and Unicode

Total characters59409588
Distinct characters36
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowExempt
2nd rowExempt
3rd rowExempt
4th rowExempt
5th rowExempt

Common Values

ValueCountFrequency (%)
Healthy (<36%) 1359589
38.5%
Manageable (36-42%) 888042
25.1%
Unmanageable (43-49%) 822358
23.3%
Struggling (>50%) 326996
 
9.3%
Exempt 100070
 
2.8%
NA 36837
 
1.0%

Length

2024-04-08T11:07:10.715926image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-08T11:07:10.786547image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
healthy 1359589
19.6%
36 1359589
19.6%
manageable 888042
12.8%
36-42 888042
12.8%
unmanageable 822358
11.9%
43-49 822358
11.9%
struggling 326996
 
4.7%
50 326996
 
4.7%
exempt 100070
 
1.4%
na 36837
 
0.5%

Most occurring characters

ValueCountFrequency (%)
a 6490789
 
10.9%
e 4880459
 
8.2%
l 3396985
 
5.7%
3396985
 
5.7%
( 3396985
 
5.7%
% 3396985
 
5.7%
) 3396985
 
5.7%
3 3069989
 
5.2%
n 2859754
 
4.8%
g 2691388
 
4.5%
Other values (26) 22432284
37.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 28639164
48.2%
Decimal Number 10214770
 
17.2%
Uppercase Letter 3570729
 
6.0%
Space Separator 3396985
 
5.7%
Open Punctuation 3396985
 
5.7%
Other Punctuation 3396985
 
5.7%
Close Punctuation 3396985
 
5.7%
Dash Punctuation 1710400
 
2.9%
Math Symbol 1686585
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 6490789
22.7%
e 4880459
17.0%
l 3396985
11.9%
n 2859754
10.0%
g 2691388
9.4%
t 1786655
 
6.2%
b 1710400
 
6.0%
y 1359589
 
4.7%
h 1359589
 
4.7%
m 922428
 
3.2%
Other values (5) 1181128
 
4.1%
Decimal Number
ValueCountFrequency (%)
3 3069989
30.1%
4 2532758
24.8%
6 2247631
22.0%
2 888042
 
8.7%
9 822358
 
8.1%
0 326996
 
3.2%
5 326996
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
H 1359589
38.1%
M 888042
24.9%
U 822358
23.0%
S 326996
 
9.2%
E 100070
 
2.8%
N 36837
 
1.0%
A 36837
 
1.0%
Math Symbol
ValueCountFrequency (%)
< 1359589
80.6%
> 326996
 
19.4%
Space Separator
ValueCountFrequency (%)
3396985
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3396985
100.0%
Other Punctuation
ValueCountFrequency (%)
% 3396985
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3396985
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1710400
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 32209893
54.2%
Common 27199695
45.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 6490789
20.2%
e 4880459
15.2%
l 3396985
10.5%
n 2859754
8.9%
g 2691388
8.4%
t 1786655
 
5.5%
b 1710400
 
5.3%
H 1359589
 
4.2%
y 1359589
 
4.2%
h 1359589
 
4.2%
Other values (12) 4314696
13.4%
Common
ValueCountFrequency (%)
3396985
12.5%
( 3396985
12.5%
% 3396985
12.5%
) 3396985
12.5%
3 3069989
11.3%
4 2532758
9.3%
6 2247631
8.3%
- 1710400
6.3%
< 1359589
5.0%
2 888042
 
3.3%
Other values (4) 1803346
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 59409588
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 6490789
 
10.9%
e 4880459
 
8.2%
l 3396985
 
5.7%
3396985
 
5.7%
( 3396985
 
5.7%
% 3396985
 
5.7%
) 3396985
 
5.7%
3 3069989
 
5.2%
n 2859754
 
4.8%
g 2691388
 
4.5%
Other values (26) 22432284
37.8%

combined_loan_to_value_ratio
Real number (ℝ)

MISSING  SKEWED 

Distinct94184
Distinct (%)2.8%
Missing188622
Missing (%)5.3%
Infinite0
Infinite (%)0.0%
Mean114.10964
Minimum0.2
Maximum61224490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size53.9 MiB
2024-04-08T11:07:10.870318image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile54.545
Q180
median90.323
Q396.5
95-th percentile100
Maximum61224490
Range61224490
Interquartile range (IQR)16.5

Descriptive statistics

Standard deviation34791.559
Coefficient of variation (CV)304.89587
Kurtosis2875590.9
Mean114.10964
Median Absolute Deviation (MAD)6.677
Skewness1654.3121
Sum3.8172757 × 108
Variance1.2104526 × 109
MonotonicityNot monotonic
2024-04-08T11:07:10.959370image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 574948
16.3%
96.5 477727
13.5%
95 457784
13.0%
90 231637
 
6.6%
97 222492
 
6.3%
85 76875
 
2.2%
100 70238
 
2.0%
75 60473
 
1.7%
70 28159
 
0.8%
98.188 13998
 
0.4%
Other values (94174) 1130939
32.0%
(Missing) 188622
 
5.3%
ValueCountFrequency (%)
0.2 2
< 0.1%
0.49 1
< 0.1%
0.52 1
< 0.1%
0.53 1
< 0.1%
0.58 1
< 0.1%
0.723 1
< 0.1%
0.76 1
< 0.1%
0.8 1
< 0.1%
0.81 1
< 0.1%
0.833 1
< 0.1%
ValueCountFrequency (%)
61224489.8 1
 
< 0.1%
13384132.93 1
 
< 0.1%
10400000 1
 
< 0.1%
3500000 1
 
< 0.1%
993446 1
 
< 0.1%
325785.714 1
 
< 0.1%
100000 2
 
< 0.1%
98189 6
< 0.1%
98000 1
 
< 0.1%
97749.875 1
 
< 0.1%

main_underwriter
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.9 MiB
Desktop Underwriter
1857165 
Loan Prospector
488201 
Technology Open to Approved Lenders
450317 
Not Applicable
441373 
No main Aus
208340 
Other values (2)
 
88496

Length

Max length35
Median length19
Mean length19.042627
Min length5

Characters and Unicode

Total characters67294587
Distinct characters32
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Applicable
2nd rowNot Applicable
3rd rowNot Applicable
4th rowNot Applicable
5th rowNot Applicable

Common Values

ValueCountFrequency (%)
Desktop Underwriter 1857165
52.6%
Loan Prospector 488201
 
13.8%
Technology Open to Approved Lenders 450317
 
12.7%
Not Applicable 441373
 
12.5%
No main Aus 208340
 
5.9%
Other 88060
 
2.5%
Guaranteed Underwriting System 436
 
< 0.1%

Length

2024-04-08T11:07:11.048831image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-08T11:07:11.279884image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
desktop 1857165
21.7%
underwriter 1857165
21.7%
loan 488201
 
5.7%
prospector 488201
 
5.7%
technology 450317
 
5.3%
open 450317
 
5.3%
to 450317
 
5.3%
approved 450317
 
5.3%
lenders 450317
 
5.3%
applicable 441373
 
5.2%
Other values (8) 1155761
13.5%

Most occurring characters

ValueCountFrequency (%)
e 8842458
13.1%
r 7537899
11.2%
o 5772749
 
8.6%
t 5183589
 
7.7%
5005559
 
7.4%
p 4579063
 
6.8%
n 3905965
 
5.8%
s 3004459
 
4.5%
d 2758671
 
4.1%
i 2507750
 
3.7%
Other values (22) 18196425
27.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 54408234
80.9%
Uppercase Letter 7880794
 
11.7%
Space Separator 5005559
 
7.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8842458
16.3%
r 7537899
13.9%
o 5772749
10.6%
t 5183589
9.5%
p 4579063
8.4%
n 3905965
7.2%
s 3004459
 
5.5%
d 2758671
 
5.1%
i 2507750
 
4.6%
w 1857601
 
3.4%
Other values (11) 8458030
15.5%
Uppercase Letter
ValueCountFrequency (%)
U 1857601
23.6%
D 1857165
23.6%
A 1100030
14.0%
L 938518
11.9%
N 649713
 
8.2%
O 538377
 
6.8%
P 488201
 
6.2%
T 450317
 
5.7%
G 436
 
< 0.1%
S 436
 
< 0.1%
Space Separator
ValueCountFrequency (%)
5005559
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 62289028
92.6%
Common 5005559
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8842458
14.2%
r 7537899
12.1%
o 5772749
 
9.3%
t 5183589
 
8.3%
p 4579063
 
7.4%
n 3905965
 
6.3%
s 3004459
 
4.8%
d 2758671
 
4.4%
i 2507750
 
4.0%
w 1857601
 
3.0%
Other values (21) 16338824
26.2%
Common
ValueCountFrequency (%)
5005559
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67294587
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8842458
13.1%
r 7537899
11.2%
o 5772749
 
8.6%
t 5183589
 
7.7%
5005559
 
7.4%
p 4579063
 
6.8%
n 3905965
 
5.8%
s 3004459
 
4.5%
d 2758671
 
4.1%
i 2507750
 
3.7%
Other values (22) 18196425
27.0%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.9 MiB
Middle (80-120%)
1520918 
Upper (>120%)
1386851 
Moderate (50-80%)
513750 
Low (<50%)
 
83585
NA
 
28788

Length

Max length17
Median length16
Mean length14.712087
Min length2

Characters and Unicode

Total characters51990927
Distinct characters27
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMiddle (80-120%)
2nd rowMiddle (80-120%)
3rd rowMiddle (80-120%)
4th rowMiddle (80-120%)
5th rowMiddle (80-120%)

Common Values

ValueCountFrequency (%)
Middle (80-120%) 1520918
43.0%
Upper (>120%) 1386851
39.2%
Moderate (50-80%) 513750
 
14.5%
Low (<50%) 83585
 
2.4%
NA 28788
 
0.8%

Length

2024-04-08T11:07:11.364274image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-08T11:07:11.432707image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
middle 1520918
21.6%
80-120 1520918
21.6%
upper 1386851
19.7%
120 1386851
19.7%
moderate 513750
 
7.3%
50-80 513750
 
7.3%
low 83585
 
1.2%
50 83585
 
1.2%
na 28788
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 5539772
 
10.7%
e 3935269
 
7.6%
d 3555586
 
6.8%
) 3505104
 
6.7%
3505104
 
6.7%
( 3505104
 
6.7%
% 3505104
 
6.7%
1 2907769
 
5.6%
2 2907769
 
5.6%
p 2773702
 
5.3%
Other values (17) 16350644
31.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16915414
32.5%
Decimal Number 13987313
26.9%
Uppercase Letter 3562680
 
6.9%
Close Punctuation 3505104
 
6.7%
Space Separator 3505104
 
6.7%
Open Punctuation 3505104
 
6.7%
Other Punctuation 3505104
 
6.7%
Dash Punctuation 2034668
 
3.9%
Math Symbol 1470436
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3935269
23.3%
d 3555586
21.0%
p 2773702
16.4%
r 1900601
11.2%
i 1520918
 
9.0%
l 1520918
 
9.0%
o 597335
 
3.5%
a 513750
 
3.0%
t 513750
 
3.0%
w 83585
 
0.5%
Decimal Number
ValueCountFrequency (%)
0 5539772
39.6%
1 2907769
20.8%
2 2907769
20.8%
8 2034668
 
14.5%
5 597335
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
M 2034668
57.1%
U 1386851
38.9%
L 83585
 
2.3%
N 28788
 
0.8%
A 28788
 
0.8%
Math Symbol
ValueCountFrequency (%)
> 1386851
94.3%
< 83585
 
5.7%
Close Punctuation
ValueCountFrequency (%)
) 3505104
100.0%
Space Separator
ValueCountFrequency (%)
3505104
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3505104
100.0%
Other Punctuation
ValueCountFrequency (%)
% 3505104
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2034668
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 31512833
60.6%
Latin 20478094
39.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3935269
19.2%
d 3555586
17.4%
p 2773702
13.5%
M 2034668
9.9%
r 1900601
9.3%
i 1520918
 
7.4%
l 1520918
 
7.4%
U 1386851
 
6.8%
o 597335
 
2.9%
a 513750
 
2.5%
Other values (5) 738496
 
3.6%
Common
ValueCountFrequency (%)
0 5539772
17.6%
) 3505104
11.1%
3505104
11.1%
( 3505104
11.1%
% 3505104
11.1%
1 2907769
9.2%
2 2907769
9.2%
- 2034668
 
6.5%
8 2034668
 
6.5%
> 1386851
 
4.4%
Other values (2) 680920
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51990927
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5539772
 
10.7%
e 3935269
 
7.6%
d 3555586
 
6.8%
) 3505104
 
6.7%
3505104
 
6.7%
( 3505104
 
6.7%
% 3505104
 
6.7%
1 2907769
 
5.6%
2 2907769
 
5.6%
p 2773702
 
5.3%
Other values (17) 16350644
31.4%

lender_type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.9 MiB
Independent Mortgage Companies
2099141 
Banks
1154250 
Credit Union
269945 
No definition
 
10556

Length

Max length30
Median length30
Mean length20.408673
Min length5

Characters and Unicode

Total characters72122048
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBanks
2nd rowBanks
3rd rowBanks
4th rowBanks
5th rowBanks

Common Values

ValueCountFrequency (%)
Independent Mortgage Companies 2099141
59.4%
Banks 1154250
32.7%
Credit Union 269945
 
7.6%
No definition 10556
 
0.3%

Length

2024-04-08T11:07:11.504045image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-08T11:07:11.568562image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
independent 2099141
26.2%
mortgage 2099141
26.2%
companies 2099141
26.2%
banks 1154250
14.4%
credit 269945
 
3.4%
union 269945
 
3.4%
no 10556
 
0.1%
definition 10556
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 10776206
14.9%
n 10111816
14.0%
a 5352532
 
7.4%
o 4489339
 
6.2%
d 4478783
 
6.2%
t 4478783
 
6.2%
4478783
 
6.2%
g 4198282
 
5.8%
p 4198282
 
5.8%
s 3253391
 
4.5%
Other values (11) 16305851
22.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 59641146
82.7%
Uppercase Letter 8002119
 
11.1%
Space Separator 4478783
 
6.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 10776206
18.1%
n 10111816
17.0%
a 5352532
9.0%
o 4489339
7.5%
d 4478783
7.5%
t 4478783
7.5%
g 4198282
 
7.0%
p 4198282
 
7.0%
s 3253391
 
5.5%
i 2670699
 
4.5%
Other values (4) 5633033
9.4%
Uppercase Letter
ValueCountFrequency (%)
C 2369086
29.6%
I 2099141
26.2%
M 2099141
26.2%
B 1154250
14.4%
U 269945
 
3.4%
N 10556
 
0.1%
Space Separator
ValueCountFrequency (%)
4478783
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 67643265
93.8%
Common 4478783
 
6.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 10776206
15.9%
n 10111816
14.9%
a 5352532
 
7.9%
o 4489339
 
6.6%
d 4478783
 
6.6%
t 4478783
 
6.6%
g 4198282
 
6.2%
p 4198282
 
6.2%
s 3253391
 
4.8%
i 2670699
 
3.9%
Other values (10) 13635152
20.2%
Common
ValueCountFrequency (%)
4478783
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72122048
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 10776206
14.9%
n 10111816
14.0%
a 5352532
 
7.4%
o 4489339
 
6.2%
d 4478783
 
6.2%
t 4478783
 
6.2%
4478783
 
6.2%
g 4198282
 
5.8%
p 4198282
 
5.8%
s 3253391
 
4.5%
Other values (11) 16305851
22.6%

lender_size
Real number (ℝ)

Distinct1984
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean149673.45
Minimum1
Maximum1026755
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size53.9 MiB
2024-04-08T11:07:11.646209image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile696
Q15788
median26178
Q3160012
95-th percentile774905
Maximum1026755
Range1026754
Interquartile range (IQR)154224

Descriptive statistics

Standard deviation248883.61
Coefficient of variation (CV)1.6628441
Kurtosis3.994445
Mean149673.45
Median Absolute Deviation (MAD)25011
Skewness2.1595154
Sum5.2892982 × 1011
Variance6.1943053 × 1010
MonotonicityNot monotonic
2024-04-08T11:07:11.734571image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
410835 146341
 
4.1%
774905 143112
 
4.0%
1026755 117048
 
3.3%
198516 83237
 
2.4%
466552 68520
 
1.9%
527621 68062
 
1.9%
282102 64455
 
1.8%
257847 50048
 
1.4%
130400 46138
 
1.3%
380650 41667
 
1.2%
Other values (1974) 2705264
76.6%
ValueCountFrequency (%)
1 2
 
< 0.1%
2 3
 
< 0.1%
4 2
 
< 0.1%
5 6
< 0.1%
7 3
 
< 0.1%
8 8
< 0.1%
9 5
< 0.1%
10 10
< 0.1%
11 9
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
1026755 117048
3.3%
774905 143112
4.0%
527621 68062
1.9%
466552 68520
1.9%
410835 146341
4.1%
380650 41667
 
1.2%
308884 20575
 
0.6%
302784 8643
 
0.2%
282102 64455
1.8%
257847 50048
 
1.4%

white_population_pct
Real number (ℝ)

Distinct70236
Distinct (%)2.0%
Missing24986
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean66.847609
Minimum0
Maximum100
Zeros4183
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size53.9 MiB
2024-04-08T11:07:11.822557image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13.468089
Q152.184386
median74.042918
Q386.895476
95-th percentile95.595182
Maximum100
Range100
Interquartile range (IQR)34.71109

Descriptive statistics

Standard deviation25.179598
Coefficient of variation (CV)0.37667163
Kurtosis-0.090310374
Mean66.847609
Median Absolute Deviation (MAD)15.335483
Skewness-0.91371837
Sum2.3456198 × 108
Variance634.01214
MonotonicityNot monotonic
2024-04-08T11:07:11.907406image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4183
 
0.1%
25.08549218 1822
 
0.1%
38.83451625 1639
 
< 0.1%
64.63691767 1429
 
< 0.1%
69.87282823 1224
 
< 0.1%
71.10172718 1213
 
< 0.1%
42.08020433 1202
 
< 0.1%
53.94154736 1202
 
< 0.1%
75.02294104 1170
 
< 0.1%
83.06857931 1079
 
< 0.1%
Other values (70226) 3492743
98.8%
(Missing) 24986
 
0.7%
ValueCountFrequency (%)
0 4183
0.1%
0.01163196464 15
 
< 0.1%
0.01282709082 3
 
< 0.1%
0.01891431814 85
 
< 0.1%
0.02134927412 1
 
< 0.1%
0.02161694769 2
 
< 0.1%
0.02297794118 15
 
< 0.1%
0.02919708029 4
 
< 0.1%
0.03082614057 16
 
< 0.1%
0.03355704698 3
 
< 0.1%
ValueCountFrequency (%)
100 823
< 0.1%
99.96020692 7
 
< 0.1%
99.95645548 31
 
< 0.1%
99.93152248 22
 
< 0.1%
99.92146597 22
 
< 0.1%
99.92142483 5
 
< 0.1%
99.91421218 36
 
< 0.1%
99.90821478 12
 
< 0.1%
99.89059081 21
 
< 0.1%
99.88502443 46
 
< 0.1%

metro_name
Text

MISSING 

Distinct959
Distinct (%)< 0.1%
Missing131511
Missing (%)3.7%
Memory size53.9 MiB
2024-04-08T11:07:12.070021image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length49
Median length36
Mean length24.948878
Min length7

Characters and Unicode

Total characters84885590
Distinct characters62
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAmes, IA
2nd rowMason City, IA
3rd rowMason City, IA
4th rowAlbert Lea, MN
5th rowAmes, IA
ValueCountFrequency (%)
tx 310310
 
3.5%
ca 302059
 
3.4%
fl 283395
 
3.2%
ga 116800
 
1.3%
city 115106
 
1.3%
il 114524
 
1.3%
pa 112277
 
1.3%
new 111538
 
1.2%
mi 104344
 
1.2%
az 103039
 
1.2%
Other values (1079) 7261084
81.3%
2024-04-08T11:07:12.300789image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 6183985
 
7.3%
e 5545254
 
6.5%
5532095
 
6.5%
n 5378296
 
6.3%
o 5047813
 
5.9%
- 4672915
 
5.5%
r 4107846
 
4.8%
l 3725970
 
4.4%
i 3663637
 
4.3%
t 3459004
 
4.1%
Other values (52) 37568775
44.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 53385226
62.9%
Uppercase Letter 17718898
 
20.9%
Space Separator 5532095
 
6.5%
Dash Punctuation 4672915
 
5.5%
Other Punctuation 3576456
 
4.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 6183985
11.6%
e 5545254
10.4%
n 5378296
10.1%
o 5047813
9.5%
r 4107846
 
7.7%
l 3725970
 
7.0%
i 3663637
 
6.9%
t 3459004
 
6.5%
s 2931410
 
5.5%
d 1856888
 
3.5%
Other values (20) 11485123
21.5%
Uppercase Letter
ValueCountFrequency (%)
C 1969155
 
11.1%
A 1897913
 
10.7%
N 1301318
 
7.3%
S 1210554
 
6.8%
L 1128171
 
6.4%
M 1050505
 
5.9%
T 815699
 
4.6%
P 786605
 
4.4%
W 779269
 
4.4%
B 769387
 
4.3%
Other values (16) 6010322
33.9%
Other Punctuation
ValueCountFrequency (%)
, 3402381
95.1%
. 153416
 
4.3%
/ 17761
 
0.5%
' 2898
 
0.1%
Space Separator
ValueCountFrequency (%)
5532095
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4672915
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 71104124
83.8%
Common 13781466
 
16.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 6183985
 
8.7%
e 5545254
 
7.8%
n 5378296
 
7.6%
o 5047813
 
7.1%
r 4107846
 
5.8%
l 3725970
 
5.2%
i 3663637
 
5.2%
t 3459004
 
4.9%
s 2931410
 
4.1%
C 1969155
 
2.8%
Other values (46) 29091754
40.9%
Common
ValueCountFrequency (%)
5532095
40.1%
- 4672915
33.9%
, 3402381
24.7%
. 153416
 
1.1%
/ 17761
 
0.1%
' 2898
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84877388
> 99.9%
None 8202
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 6183985
 
7.3%
e 5545254
 
6.5%
5532095
 
6.5%
n 5378296
 
6.3%
o 5047813
 
5.9%
- 4672915
 
5.5%
r 4107846
 
4.8%
l 3725970
 
4.4%
i 3663637
 
4.3%
t 3459004
 
4.1%
Other values (48) 37560573
44.3%
None
ValueCountFrequency (%)
ó 7303
89.0%
ñ 551
 
6.7%
á 209
 
2.5%
ü 139
 
1.7%
Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.9 MiB
90th percentile
1252242 
80th percentile
583803 
99th percentile
371328 
70th percentile
348534 
Micro area
202004 
Other values (7)
775981 

Length

Max length15
Median length15
Mean length14.667116
Min length10

Characters and Unicode

Total characters51832003
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0th percentile
2nd row0th percentile
3rd row10th percentile
4th row0th percentile
5th row0th percentile

Common Values

ValueCountFrequency (%)
90th percentile 1252242
35.4%
80th percentile 583803
16.5%
99th percentile 371328
 
10.5%
70th percentile 348534
 
9.9%
Micro area 202004
 
5.7%
60th percentile 195259
 
5.5%
0th percentile 166357
 
4.7%
50th percentile 133795
 
3.8%
40th percentile 97165
 
2.7%
30th percentile 78160
 
2.2%
Other values (2) 105245
 
3.0%

Length

2024-04-08T11:07:12.380720image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
percentile 3331888
47.1%
90th 1252242
 
17.7%
80th 583803
 
8.3%
99th 371328
 
5.3%
70th 348534
 
4.9%
micro 202004
 
2.9%
area 202004
 
2.9%
60th 195259
 
2.8%
0th 166357
 
2.4%
50th 133795
 
1.9%
Other values (4) 280570
 
4.0%

Most occurring characters

ValueCountFrequency (%)
e 10197668
19.7%
t 6663776
12.9%
r 3735896
 
7.2%
3533892
 
6.8%
c 3533892
 
6.8%
i 3533892
 
6.8%
l 3331888
 
6.4%
h 3331888
 
6.4%
p 3331888
 
6.4%
n 3331888
 
6.4%
Other values (13) 7305435
14.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 41598688
80.3%
Decimal Number 6497419
 
12.5%
Space Separator 3533892
 
6.8%
Uppercase Letter 202004
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 10197668
24.5%
t 6663776
16.0%
r 3735896
 
9.0%
c 3533892
 
8.5%
i 3533892
 
8.5%
l 3331888
 
8.0%
h 3331888
 
8.0%
p 3331888
 
8.0%
n 3331888
 
8.0%
a 404008
 
1.0%
Decimal Number
ValueCountFrequency (%)
0 2960560
45.6%
9 1994898
30.7%
8 583803
 
9.0%
7 348534
 
5.4%
6 195259
 
3.0%
5 133795
 
2.1%
4 97165
 
1.5%
3 78160
 
1.2%
2 58483
 
0.9%
1 46762
 
0.7%
Space Separator
ValueCountFrequency (%)
3533892
100.0%
Uppercase Letter
ValueCountFrequency (%)
M 202004
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 41800692
80.6%
Common 10031311
 
19.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 10197668
24.4%
t 6663776
15.9%
r 3735896
 
8.9%
c 3533892
 
8.5%
i 3533892
 
8.5%
l 3331888
 
8.0%
h 3331888
 
8.0%
p 3331888
 
8.0%
n 3331888
 
8.0%
a 404008
 
1.0%
Other values (2) 404008
 
1.0%
Common
ValueCountFrequency (%)
3533892
35.2%
0 2960560
29.5%
9 1994898
19.9%
8 583803
 
5.8%
7 348534
 
3.5%
6 195259
 
1.9%
5 133795
 
1.3%
4 97165
 
1.0%
3 78160
 
0.8%
2 58483
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51832003
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 10197668
19.7%
t 6663776
12.9%
r 3735896
 
7.2%
3533892
 
6.8%
c 3533892
 
6.8%
i 3533892
 
6.8%
l 3331888
 
6.4%
h 3331888
 
6.4%
p 3331888
 
6.4%
n 3331888
 
6.4%
Other values (13) 7305435
14.1%

state_code
Real number (ℝ)

Distinct52
Distinct (%)< 0.1%
Missing24438
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean28.016897
Minimum1
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size53.9 MiB
2024-04-08T11:07:12.441632image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q112
median27
Q342
95-th percentile53
Maximum72
Range71
Interquartile range (IQR)30

Descriptive statistics

Standard deviation16.28383
Coefficient of variation (CV)0.58121464
Kurtosis-1.2835181
Mean28.016897
Median Absolute Deviation (MAD)15
Skewness0.054109569
Sum98324010
Variance265.16313
MonotonicityNot monotonic
2024-04-08T11:07:12.523930image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 319004
 
9.0%
6 304379
 
8.6%
12 284738
 
8.1%
17 136024
 
3.8%
39 132836
 
3.8%
36 130346
 
3.7%
13 128627
 
3.6%
42 124632
 
3.5%
37 123998
 
3.5%
26 111038
 
3.1%
Other values (42) 1713832
48.5%
ValueCountFrequency (%)
1 47561
 
1.3%
2 6046
 
0.2%
4 103213
 
2.9%
5 26752
 
0.8%
6 304379
8.6%
8 89114
 
2.5%
9 37986
 
1.1%
10 11552
 
0.3%
11 7693
 
0.2%
12 284738
8.1%
ValueCountFrequency (%)
72 9058
 
0.3%
56 5723
 
0.2%
55 66622
 
1.9%
54 12450
 
0.4%
53 95381
 
2.7%
51 93976
 
2.7%
50 5331
 
0.2%
49 49623
 
1.4%
48 319004
9.0%
47 80198
 
2.3%

county_code
Real number (ℝ)

Distinct321
Distinct (%)< 0.1%
Missing24438
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean87.691435
Minimum1
Maximum840
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size53.9 MiB
2024-04-08T11:07:12.609463image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q129
median65
Q3111
95-th percentile217
Maximum840
Range839
Interquartile range (IQR)82

Descriptive statistics

Standard deviation98.908476
Coefficient of variation (CV)1.1279149
Kurtosis14.265155
Mean87.691435
Median Absolute Deviation (MAD)40
Skewness3.2115213
Sum3.0774906 × 108
Variance9782.8867
MonotonicityNot monotonic
2024-04-08T11:07:12.687773image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 123326
 
3.5%
3 113539
 
3.2%
31 113474
 
3.2%
37 87428
 
2.5%
1 83268
 
2.4%
59 70644
 
2.0%
5 70375
 
2.0%
29 63508
 
1.8%
35 62245
 
1.8%
11 61390
 
1.7%
Other values (311) 2660257
75.3%
ValueCountFrequency (%)
1 83268
2.4%
3 113539
3.2%
5 70375
2.0%
6 46
 
< 0.1%
7 35986
 
1.0%
9 44933
 
1.3%
11 61390
1.7%
12 52
 
< 0.1%
13 123326
3.5%
14 1290
 
< 0.1%
ValueCountFrequency (%)
840 224
 
< 0.1%
830 112
 
< 0.1%
820 355
 
< 0.1%
810 4444
0.1%
800 980
 
< 0.1%
790 333
 
< 0.1%
775 263
 
< 0.1%
770 1087
 
< 0.1%
760 2455
0.1%
750 72
 
< 0.1%
Distinct120156
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size53.9 MiB
2024-04-08T11:07:12.857919image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length13
Median length13
Mean length12.664663
Min length2

Characters and Unicode

Total characters44755551
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17947 ?
Unique (%)0.5%

Sample

1st row19081270100.0
2nd row19081270200.0
3rd row19169010600.0
4th row19081270100.0
5th row19081270100.0
ValueCountFrequency (%)
nan 24890
 
0.7%
48157672900.0 1722
 
< 0.1%
48201542900.0 1591
 
< 0.1%
48157673200.0 1371
 
< 0.1%
48439114103.0 1177
 
< 0.1%
48085030203.0 1161
 
< 0.1%
48157673400.0 1152
 
< 0.1%
48157673101.0 1145
 
< 0.1%
48085030305.0 1127
 
< 0.1%
48329010112.0 1024
 
< 0.1%
Other values (120146) 3497532
99.0%
2024-04-08T11:07:13.106802image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 15730394
35.1%
1 6134505
 
13.7%
3 3534557
 
7.9%
2 3491795
 
7.8%
. 3344435
 
7.5%
5 2581830
 
5.8%
4 2509643
 
5.6%
7 2064897
 
4.6%
9 1988150
 
4.4%
6 1791135
 
4.0%
Other values (4) 1584210
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 41336444
92.4%
Other Punctuation 3344435
 
7.5%
Lowercase Letter 74671
 
0.2%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15730394
38.1%
1 6134505
 
14.8%
3 3534557
 
8.6%
2 3491795
 
8.4%
5 2581830
 
6.2%
4 2509643
 
6.1%
7 2064897
 
5.0%
9 1988150
 
4.8%
6 1791135
 
4.3%
8 1509538
 
3.7%
Lowercase Letter
ValueCountFrequency (%)
n 49780
66.7%
a 24891
33.3%
Other Punctuation
ValueCountFrequency (%)
. 3344435
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 44680879
99.8%
Latin 74672
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15730394
35.2%
1 6134505
 
13.7%
3 3534557
 
7.9%
2 3491795
 
7.8%
. 3344435
 
7.5%
5 2581830
 
5.8%
4 2509643
 
5.6%
7 2064897
 
4.6%
9 1988150
 
4.4%
6 1791135
 
4.0%
Latin
ValueCountFrequency (%)
n 49780
66.7%
a 24891
33.3%
N 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44755551
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15730394
35.1%
1 6134505
 
13.7%
3 3534557
 
7.9%
2 3491795
 
7.8%
. 3344435
 
7.5%
5 2581830
 
5.8%
4 2509643
 
5.6%
7 2064897
 
4.6%
9 1988150
 
4.4%
6 1791135
 
4.0%
Other values (4) 1584210
 
3.5%

activity_year
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.9 MiB
2019
3533892 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters14135568
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019 3533892
100.0%

Length

2024-04-08T11:07:13.185149image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-08T11:07:13.231576image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2019 3533892
100.0%

Most occurring characters

ValueCountFrequency (%)
2 3533892
25.0%
0 3533892
25.0%
1 3533892
25.0%
9 3533892
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14135568
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 3533892
25.0%
0 3533892
25.0%
1 3533892
25.0%
9 3533892
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14135568
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 3533892
25.0%
0 3533892
25.0%
1 3533892
25.0%
9 3533892
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14135568
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 3533892
25.0%
0 3533892
25.0%
1 3533892
25.0%
9 3533892
25.0%

loan_outcome
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.9 MiB
Loan originated (approved)
3215722 
Loan denied
 
318170

Length

Max length26
Median length26
Mean length24.649492
Min length11

Characters and Unicode

Total characters87108642
Distinct characters15
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLoan originated (approved)
2nd rowLoan originated (approved)
3rd rowLoan originated (approved)
4th rowLoan originated (approved)
5th rowLoan originated (approved)

Common Values

ValueCountFrequency (%)
Loan originated (approved) 3215722
91.0%
Loan denied 318170
 
9.0%

Length

2024-04-08T11:07:13.291688image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-08T11:07:13.347644image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
loan 3533892
34.4%
originated 3215722
31.3%
approved 3215722
31.3%
denied 318170
 
3.1%

Most occurring characters

ValueCountFrequency (%)
o 9965336
11.4%
a 9965336
11.4%
n 7067784
8.1%
e 7067784
8.1%
d 7067784
8.1%
6749614
7.7%
i 6749614
7.7%
r 6431444
 
7.4%
p 6431444
 
7.4%
L 3533892
 
4.1%
Other values (5) 16078610
18.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 70393692
80.8%
Space Separator 6749614
 
7.7%
Uppercase Letter 3533892
 
4.1%
Open Punctuation 3215722
 
3.7%
Close Punctuation 3215722
 
3.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 9965336
14.2%
a 9965336
14.2%
n 7067784
10.0%
e 7067784
10.0%
d 7067784
10.0%
i 6749614
9.6%
r 6431444
9.1%
p 6431444
9.1%
g 3215722
 
4.6%
t 3215722
 
4.6%
Space Separator
ValueCountFrequency (%)
6749614
100.0%
Uppercase Letter
ValueCountFrequency (%)
L 3533892
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3215722
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3215722
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 73927584
84.9%
Common 13181058
 
15.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 9965336
13.5%
a 9965336
13.5%
n 7067784
9.6%
e 7067784
9.6%
d 7067784
9.6%
i 6749614
9.1%
r 6431444
8.7%
p 6431444
8.7%
L 3533892
 
4.8%
g 3215722
 
4.3%
Other values (2) 6431444
8.7%
Common
ValueCountFrequency (%)
6749614
51.2%
( 3215722
24.4%
) 3215722
24.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 87108642
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 9965336
11.4%
a 9965336
11.4%
n 7067784
8.1%
e 7067784
8.1%
d 7067784
8.1%
6749614
7.7%
i 6749614
7.7%
r 6431444
 
7.4%
p 6431444
 
7.4%
L 3533892
 
4.1%
Other values (5) 16078610
18.5%
Distinct5101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size53.9 MiB
2024-04-08T11:07:13.550974image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters70677840
Distinct characters36
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)< 0.1%

Sample

1st row25490003YGASV5ENH153
2nd row25490003YGASV5ENH153
3rd row25490003YGASV5ENH153
4th row25490003YGASV5ENH153
5th row25490003YGASV5ENH153
ValueCountFrequency (%)
549300hw662mn1wu8550 146341
 
4.1%
549300fgxn1k3hlb1r50 143112
 
4.0%
kb1h1dsprfmymcufxt09 117048
 
3.3%
549300mgpzblqdil7538 83237
 
2.4%
b4tydeb6gkmzo031mb27 68520
 
1.9%
7h6glxdrugqfu57rne97 68062
 
1.9%
549300j7xkt2bi5wx213 64455
 
1.8%
549300ag64nhilb7zp05 50048
 
1.4%
549300u3721pjgqzyy68 46138
 
1.3%
6byl5qzybdk8s7l73m02 41667
 
1.2%
Other values (5091) 2705264
76.6%
2024-04-08T11:07:13.826291image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 7769172
 
11.0%
5 5023608
 
7.1%
3 4597868
 
6.5%
4 4492322
 
6.4%
9 3989064
 
5.6%
1 2588454
 
3.7%
2 2217796
 
3.1%
7 2073209
 
2.9%
6 2040331
 
2.9%
8 1685160
 
2.4%
Other values (26) 34200856
48.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 36476984
51.6%
Uppercase Letter 34200856
48.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 1643802
 
4.8%
H 1614982
 
4.7%
M 1609071
 
4.7%
N 1568954
 
4.6%
D 1565974
 
4.6%
R 1565240
 
4.6%
W 1481396
 
4.3%
S 1462324
 
4.3%
K 1440948
 
4.2%
L 1440661
 
4.2%
Other values (16) 18807504
55.0%
Decimal Number
ValueCountFrequency (%)
0 7769172
21.3%
5 5023608
13.8%
3 4597868
12.6%
4 4492322
12.3%
9 3989064
10.9%
1 2588454
 
7.1%
2 2217796
 
6.1%
7 2073209
 
5.7%
6 2040331
 
5.6%
8 1685160
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
Common 36476984
51.6%
Latin 34200856
48.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 1643802
 
4.8%
H 1614982
 
4.7%
M 1609071
 
4.7%
N 1568954
 
4.6%
D 1565974
 
4.6%
R 1565240
 
4.6%
W 1481396
 
4.3%
S 1462324
 
4.3%
K 1440948
 
4.2%
L 1440661
 
4.2%
Other values (16) 18807504
55.0%
Common
ValueCountFrequency (%)
0 7769172
21.3%
5 5023608
13.8%
3 4597868
12.6%
4 4492322
12.3%
9 3989064
10.9%
1 2588454
 
7.1%
2 2217796
 
6.1%
7 2073209
 
5.7%
6 2040331
 
5.6%
8 1685160
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70677840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7769172
 
11.0%
5 5023608
 
7.1%
3 4597868
 
6.5%
4 4492322
 
6.4%
9 3989064
 
5.6%
1 2588454
 
3.7%
2 2217796
 
3.1%
7 2073209
 
2.9%
6 2040331
 
2.9%
8 1685160
 
2.4%
Other values (26) 34200856
48.4%

Interactions

2024-04-08T11:06:47.403448image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:29.525413image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:31.993607image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:34.346447image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:36.947047image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:39.856422image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:42.276363image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:44.857361image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:47.785000image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:29.812215image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:32.249959image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:34.650290image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:37.335046image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:40.151823image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:42.611441image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:45.192272image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:48.196721image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:30.232412image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:32.549042image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:34.929035image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:37.719924image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:40.452299image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:42.922512image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:45.497915image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:48.557369image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:30.555257image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:32.855640image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:35.236531image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:38.102016image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:40.761485image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:43.243225image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:45.819174image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:48.893621image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:30.841864image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:33.152430image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:35.543537image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:38.504918image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:41.024965image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:43.585646image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:46.153485image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:49.205473image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:31.141395image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:33.453627image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:35.865456image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:38.892356image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:41.334127image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:43.896170image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:46.470647image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:49.550836image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:31.428111image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:33.756624image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:36.214218image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:39.265309image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:41.652251image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:44.211632image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:46.774145image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:49.881859image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:31.723872image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:34.067982image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:36.571775image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:39.574837image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:41.963400image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:44.528068image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-08T11:06:47.090740image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-04-08T11:06:51.094779image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-08T11:06:55.061443image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

racesexco_applicantageincomeloan_amountproperty_value_ratiomortgage_termcredit_modeldebt_to_income_ratiocombined_loan_to_value_ratiomain_underwritertract_to_metro_income_percentagelender_typelender_sizewhite_population_pctmetro_namemetro_size_percentilestate_codecounty_codecensus_tractactivity_yearloan_outcomelender_id
0WhiteFemaleNo co-applicant25 through 3423.025000NaNNANAExemptNaNNot ApplicableMiddle (80-120%)Banks10794.487578NaN0th percentile19.081.019081270100.02019Loan originated (approved)25490003YGASV5ENH153
1WhiteMaleNo co-applicant25 through 3442.085000NaNNANAExemptNaNNot ApplicableMiddle (80-120%)Banks10793.845535NaN0th percentile19.081.019081270200.02019Loan originated (approved)25490003YGASV5ENH153
2WhiteMaleCo-applicant25 through 34125.095000NaNNANAExemptNaNNot ApplicableMiddle (80-120%)Banks10796.340348Ames, IA10th percentile19.0169.019169010600.02019Loan originated (approved)25490003YGASV5ENH153
3WhiteMaleNo co-applicantLess than 2534.075000NaNNANAExemptNaNNot ApplicableMiddle (80-120%)Banks10794.487578NaN0th percentile19.081.019081270100.02019Loan originated (approved)25490003YGASV5ENH153
4WhiteFemaleNo co-applicant25 through 3437.0145000NaNNANAExemptNaNNot ApplicableMiddle (80-120%)Banks10794.487578NaN0th percentile19.081.019081270100.02019Loan originated (approved)25490003YGASV5ENH153
5WhiteFemaleCo-applicant45 through 5457.075000NaNNANAExemptNaNNot ApplicableMiddle (80-120%)Banks10792.326835NaN0th percentile19.081.019081270300.02019Loan originated (approved)25490003YGASV5ENH153
6WhiteMaleNo co-applicantLess than 2527.065000NaNNANAExemptNaNNot ApplicableMiddle (80-120%)Banks10793.845535NaN0th percentile19.081.019081270200.02019Loan originated (approved)25490003YGASV5ENH153
7WhiteMaleNo co-applicantLess than 2532.075000NaNNANAExemptNaNNot ApplicableMiddle (80-120%)Banks10794.487578NaN0th percentile19.081.019081270100.02019Loan originated (approved)25490003YGASV5ENH153
8WhiteMaleNo co-applicant25 through 3435.025000NaNNANAExemptNaNNot ApplicableMiddle (80-120%)Banks10793.845535NaN0th percentile19.081.019081270200.02019Loan originated (approved)25490003YGASV5ENH153
9WhiteMaleCo-applicant35 through 44123.0175000NaNNANAExemptNaNNot ApplicableMiddle (80-120%)Banks10792.326835NaN0th percentile19.081.019081270300.02019Loan originated (approved)25490003YGASV5ENH153
racesexco_applicantageincomeloan_amountproperty_value_ratiomortgage_termcredit_modeldebt_to_income_ratiocombined_loan_to_value_ratiomain_underwritertract_to_metro_income_percentagelender_typelender_sizewhite_population_pctmetro_namemetro_size_percentilestate_codecounty_codecensus_tractactivity_yearloan_outcomelender_id
4456601Race NANANo co-applicant35 through 44487.014250003.56530 year mortgageExperianHealthy (<36%)85.000Not ApplicableUpper (>120%)Independent Mortgage Companies189470.298235Cambridge-Newton-Framingham, MA90th percentile25.017.025017359100.02019Loan originated (approved)549300L0OVX5O63S8C68
4456602Race NANACo-applicant25 through 3456.02150000.64630 year mortgageNAManageable (36-42%)80.000Not ApplicableLow (<50%)Independent Mortgage Companies189428.293474Cambridge-Newton-Framingham, MA90th percentile25.09.025009207200.02019Loan originated (approved)549300L0OVX5O63S8C68
4456603Race NANANo co-applicant35 through 44185.07250001.82330 year mortgageExperianManageable (36-42%)80.000Not ApplicableMiddle (80-120%)Independent Mortgage Companies189466.493169Boston, MA90th percentile25.025.025025000301.02019Loan originated (approved)549300L0OVX5O63S8C68
4456604WhiteMaleNo co-applicant55 through 64300.012750004.24430 year mortgageNAManageable (36-42%)60.000Not ApplicableUpper (>120%)Independent Mortgage Companies189453.057369Cambridge-Newton-Framingham, MA90th percentile25.017.025017358300.02019Loan originated (approved)549300L0OVX5O63S8C68
4456607Race NAMaleNo co-applicant45 through 54947.014950007.59630 year mortgageNAHealthy (<36%)46.123Not ApplicableUpper (>120%)Independent Mortgage Companies189479.474940Bridgeport-Stamford-Norwalk, CT80th percentile9.01.09001011100.02019Loan originated (approved)549300L0OVX5O63S8C68
4456608WhiteFemaleCo-applicant55 through 64196.03750000.92930 year mortgageNAHealthy (<36%)80.000Desktop UnderwriterMiddle (80-120%)Independent Mortgage Companies189480.891304Cambridge-Newton-Framingham, MA90th percentile25.017.025017317102.02019Loan originated (approved)549300L0OVX5O63S8C68
4456609Race NANACo-applicant55 through 6468.03150001.61830 year mortgageTransUnionUnmanageable (43-49%)65.235Not ApplicableUpper (>120%)Independent Mortgage Companies189489.135066Providence-Warwick, RI-MA80th percentile25.05.025005631700.02019Loan originated (approved)549300L0OVX5O63S8C68
4456611WhiteMaleCo-applicant35 through 44365.08650002.28330 year mortgageNAUnmanageable (43-49%)80.000Not ApplicableUpper (>120%)Independent Mortgage Companies189493.231994Boston, MA90th percentile25.021.025021409102.02019Loan originated (approved)549300L0OVX5O63S8C68
4456612Race NANANo co-applicant45 through 5425.0850000.33930 year mortgageTransUnionUnmanageable (43-49%)90.000Loan ProspectorLow (<50%)Independent Mortgage Companies189448.053528Worcester, MA-CT80th percentile25.027.025027710700.02019Loan originated (approved)549300L0OVX5O63S8C68
4456613WhiteFemaleCo-applicant25 through 34318.06850003.04730 year mortgageTransUnionHealthy (<36%)95.000Not ApplicableUpper (>120%)Independent Mortgage Companies189492.470277Worcester, MA-CT80th percentile25.027.025027715100.02019Loan originated (approved)549300L0OVX5O63S8C68

Duplicate rows

Most frequently occurring

racesexco_applicantageincomeloan_amountproperty_value_ratiomortgage_termcredit_modeldebt_to_income_ratiocombined_loan_to_value_ratiomain_underwritertract_to_metro_income_percentagelender_typelender_sizewhite_population_pctmetro_namemetro_size_percentilestate_codecounty_codecensus_tractactivity_yearloan_outcomelender_id# duplicates
69BlackMaleNo co-applicant45 through 5490.01350000.93730 year mortgageTransUnionHealthy (<36%)90.000Technology Open to Approved LendersMiddle (80-120%)Independent Mortgage Companies25784775.824411Pittsburgh, PA90th percentile42.03.0420035238002019Loan denied549300AG64NHILB7ZP053
101LatinoFemaleNo co-applicant45 through 54180.05750001.12630 year mortgageTransUnionStruggling (>50%)75.000Not ApplicableUpper (>120%)Independent Mortgage Companies861360.801936Anaheim-Santa Ana-Irvine, CA90th percentile6.059.06059032034.02019Loan denied254900E6AIE4Z8YQM9703
249WhiteFemaleNo co-applicantGreater than 7420.0115000NaNNANANANaNNot ApplicableMiddle (80-120%)Independent Mortgage Companies578678.602620Forest City, NCMicro area37.0161.037161960900.02019Loan denied549300QUX3LK82LO30133
0AsianFemaleCo-applicant25 through 34300.06150001.289Less than 30 yearsEquifaxHealthy (<36%)94.923Desktop UnderwriterUpper (>120%)Independent Mortgage Companies6890860.115875Washington-Arlington-Alexandria, DC-VA-MD-WV90th percentile51.0107.051107611014.02019Loan denied549300YIQ7S7Z8PIHE532
1AsianFemaleCo-applicant35 through 44180.013750002.94130 year mortgageExperianStruggling (>50%)80.000OtherUpper (>120%)Banks102675520.286396Los Angeles-Long Beach-Glendale, CA99th percentile6.037.06037431600.02019Loan deniedKB1H1DSPRFMYMCUFXT092
2AsianFemaleNo co-applicant35 through 4499.04850001.11330 year mortgageTransUnionUnmanageable (43-49%)80.000Not ApplicableMiddle (80-120%)Banks16001224.318489New York-Jersey City-White Plains, NY-NJ99th percentile36.081.036081074700.02019Loan deniedAD6GFRVSDT01YPT1CS682
3AsianFemaleNo co-applicant35 through 44350.02650001.15130 year mortgageTransUnionHealthy (<36%)75.000Not ApplicableUpper (>120%)Independent Mortgage Companies627788.827434Chicago-Naperville-Evanston, IL99th percentile17.043.017043844901.02019Loan denied549300EHQ0Y7SP41BR912
4AsianFemaleNo co-applicant45 through 5442.0135000NaNNANAExemptNaNNot ApplicableMiddle (80-120%)Banks21884.139016Ocala, FL50th percentile12.083.012083002702.02019Loan originated (approved)254900G3JF710WUIHN652
5AsianFemaleNo co-applicant55 through 64480.03050002.03630 year mortgageMore than oneHealthy (<36%)30.000Not ApplicableUpper (>120%)Banks3162937.842324Nassau County-Suffolk County, NY90th percentile36.059.036059303101.02019Loan deniedTR24TWEY5RVRQV65HD492
6AsianMaleCo-applicant25 through 34185.0150000.90330 year mortgageMore than oneHealthy (<36%)102.945Not ApplicableMiddle (80-120%)Independent Mortgage Companies351479.897910Oakland-Berkeley-Livermore, CA90th percentile6.013.06013304004.02019Loan originated (approved)5493008E4KBJCB6UKR642